Filtering on the temporal probability sequence in histogram equalization for robust speech recognition

In this paper, we propose a filter-based histogram equalization (FHEQ) approach for robust speech recognition. The FHEQ approach first represents the original acoustic feature sequence with statistic probability. Then, a temporal average (TA) filter is applied to smooth the statistic probability sequence. Finally, the filtered statistic probability sequence is transformed to form a new acoustic feature stream. Filtering on statistic probability of a feature sequence is a novel concept that can incorporate the advantages of the conventional histogram equalization (HEQ) and temporal filtering techniques for better noise robustness. Our experimental results on the Aurora-2 and Aurora-4 tasks show that FHEQ outperforms the conventional cepstral mean subtraction (CMS), cepstral mean and variance normalization (CMVN), and HEQ. Furthermore, we conducted a comparison test on TA-HEQ and HEQ-TA, which apply a TA filter to smooth acoustic features before and after the HEQ processing, respectively. The test results show that FHEQ outperforms both TA-HEQ and HEQ-TA, suggesting that filtering in probability is more effective than filtering in acoustic feature.